def _update(self):
self._clear_plot()
distances = self.matrix
if distances is not None:
method = LINKAGE[self.linkage].lower()
Z = dist_matrix_linkage(distances, linkage=method)
tree = tree_from_linkage(Z)
self.linkmatrix = Z
self.root = tree
self.top_axis.setRange(tree.value.height, 0.0)
self.bottom_axis.setRange(tree.value.height, 0.0)
if self.pruning:
self._set_displayed_root(prune(tree, level=self.max_depth))
else:
self._set_displayed_root(tree)
else:
self.linkmatrix = None
self.root = None
self._set_displayed_root(None)
self._apply_selection()
def recalculate_linkage(self):
if self.distances is not None:
self.linkage = dist_matrix_linkage(self.distances,
self.LINKAGE[self.linkage_method].lower())
# Magnitude of the spinbox's step is data-dependent
vals = sorted(self.linkage[:, 2])
low, up = vals[0], vals[-1]
step = (up - low) / 20
self.threshold_spin.setSingleStep(step)
self.threshold = np.clip(self.threshold, low, up)
self.histogram.setValues([]) # without this range breaks when changing linkages
self.histogram.setValues(vals)
self.histogram.setRegion(0, self.threshold)
self.detect_duplicates()